{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "55d7bf2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import torch\n",
    "import torchvision\n",
    "from torch.utils import data\n",
    "from torchvision import transforms\n",
    "from d2l import torch as d2l\n",
    "\n",
    "d2l.use_svg_display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "822bd3c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取数据集\n",
    "trans = transforms.ToTensor()\n",
    "mnist_train = torchvision.datasets.FashionMNIST(\n",
    "    root='../data', train=True, transform=trans, download=True)\n",
    "mnist_test = torchvision.datasets.FashionMNIST(\n",
    "    root='../data', train=False, transform=trans, download=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5bdee3a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10000)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mnist_train), len(mnist_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e503c6ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 28, 28])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist_train[0][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b188910",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取标签名称\n",
    "def get_fashion_mnist_labels(labels): #@save\n",
    "    text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
    "                   'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
    "    return [text_labels[int(i)] for i in labels]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "398c6746",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 可视化样本数据\n",
    "def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5): #@save\n",
    "    \"\"\"绘制图像列表\"\"\"\n",
    "    figsize = (num_cols * scale, num_rows * scale)\n",
    "    _, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)\n",
    "    axes = axes.flatten()\n",
    "    for i, (img, ax) in enumerate(zip(imgs, axes)):\n",
    "        if torch.is_tensor(img):\n",
    "            # 如果是张量，则转换为numpy数组\n",
    "            ax.imshow(img.numpy())\n",
    "        else:\n",
    "            # 如果是numpy数组，则直接使用\n",
    "            ax.imshow(img)\n",
    "        ax.axes.get_xaxis().set_visible(False)\n",
    "        ax.axes.get_yaxis().set_visible(False)\n",
    "        if titles:\n",
    "            ax.set_title(titles[i])\n",
    "    return axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0fc36dcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:title={'center':'ankle boot'}>,\n",
       "       <AxesSubplot:title={'center':'t-shirt'}>,\n",
       "       <AxesSubplot:title={'center':'t-shirt'}>,\n",
       "       <AxesSubplot:title={'center':'dress'}>,\n",
       "       <AxesSubplot:title={'center':'t-shirt'}>,\n",
       "       <AxesSubplot:title={'center':'pullover'}>,\n",
       "       <AxesSubplot:title={'center':'sneaker'}>,\n",
       "       <AxesSubplot:title={'center':'pullover'}>,\n",
       "       <AxesSubplot:title={'center':'sandal'}>,\n",
       "       <AxesSubplot:title={'center':'sandal'}>,\n",
       "       <AxesSubplot:title={'center':'t-shirt'}>,\n",
       "       <AxesSubplot:title={'center':'ankle boot'}>,\n",
       "       <AxesSubplot:title={'center':'sandal'}>,\n",
       "       <AxesSubplot:title={'center':'sandal'}>,\n",
       "       <AxesSubplot:title={'center':'sneaker'}>,\n",
       "       <AxesSubplot:title={'center':'ankle boot'}>,\n",
       "       <AxesSubplot:title={'center':'trouser'}>,\n",
       "       <AxesSubplot:title={'center':'t-shirt'}>], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "   <cc:Work>\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
       "    <dc:date>2025-07-07T10:50:23.986995</dc:date>\n",
       "    <dc:format>image/svg+xml</dc:format>\n",
       "    <dc:creator>\n",
       "     <cc:Agent>\n",
       "      <dc:title>Matplotlib v3.5.1, https://matplotlib.org/</dc:title>\n",
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       " <defs>\n",
       "  <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
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      "text/plain": [
       "<Figure size 900x200 with 18 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x,y = next(iter(data.DataLoader(mnist_train, batch_size=18)))\n",
    "show_images(x.reshape(18, 28, 28), 2, 9, titles=get_fashion_mnist_labels(y), scale=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f7f1cfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取小批量\n",
    "batch_size = 256\n",
    "\n",
    "def get_dataloader_workers(): #@save\n",
    "    \"\"\"使用4个进程来读取数据\"\"\"\n",
    "    return 4\n",
    "\n",
    "train_iter = data.DataLoader(mnist_train, batch_size, shuffle=True, num_workers=get_dataloader_workers())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fea018c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.49 sec\n"
     ]
    }
   ],
   "source": [
    "# 读取训练数据所需时间\n",
    "timer = d2l.Timer()\n",
    "for X, y in train_iter:\n",
    "    continue\n",
    "print(f'{timer.stop():.2f} sec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5548a9ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 1, 64, 64]) torch.float32 torch.Size([32]) torch.int64\n"
     ]
    }
   ],
   "source": [
    "# 整合所有组件\n",
    "\n",
    "def load_data_fashion_mnist(batch_size, resize=None): #@save\n",
    "    \"\"\"下载Fashion-MNIST数据集，并返回数据迭代器\"\"\"\n",
    "    trans = [transforms.ToTensor()]\n",
    "    if resize:\n",
    "        trans.insert(0, transforms.Resize(resize))\n",
    "    trans = transforms.Compose(trans)\n",
    "    mnist_train = torchvision.datasets.FashionMNIST(\n",
    "        root='../data', train=True, transform=trans, download=True)\n",
    "    mnist_test = torchvision.datasets.FashionMNIST(\n",
    "        root='../data', train=False, transform=trans, download=True)\n",
    "    return (data.DataLoader(mnist_train, batch_size, shuffle=True,\n",
    "                            num_workers=get_dataloader_workers()),\n",
    "            data.DataLoader(mnist_test, batch_size, shuffle=False,\n",
    "                            num_workers=get_dataloader_workers()))\n",
    "\n",
    "train_iter, test_iter = load_data_fashion_mnist(32, resize=64)\n",
    "# 测试数据加载\n",
    "for X, y in train_iter:\n",
    "    print(X.shape, x.dtype, y.shape, y.dtype)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff9fd877",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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